What problem does it solve? Building correct Dataform pipelines for BigQuery requires deep knowledge of SQLX syntax, incremental table semantics, source declarations, and GCS ingestion patterns. This Skill guides an AI through the full lifecycle of creating, modifying, and validating Dataform projects so pipelines compile cleanly and follow BigQuery best practices. ## Core Features & Use Cases - Pipeline Generation & Modification: Creates and edits SQLX actions, source declarations, and incremental tables, with automatic data cleaning and SQL optimization applied to every change. - Environment Setup & Validation: Verifies dataform and bq CLI installation, initializes repositories with workflow_settings.yaml, manages .df-credentials.json, and validates via dataform compile and dry runs. - GCS Ingestion & BigLake Support: Builds external tables from GCS files with resilient CSV parsing options and handles 4-part BigLake Iceberg naming through schema concatenation. - Use Case: Ask the assistant to append daily CSV files from a GCS bucket into an existing BigQuery table; it will create the external table declaration, an incremental SQLX action with cleaned and schema-aligned columns, then compile and dry-run the pipeline. ## Quick Start Ask the assistant to create a Dataform pipeline that loads data from a GCS bucket into a BigQuery table and validate it with a dry run.
